{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/train_cleaned.csv', index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_train = df['2013-10-15 00:30:00':'2013-11-14 23:45:00']\n",
    "df_test = df['2013-11-14 23:45:00':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_train = df_train.pop('kwh')\n",
    "X_train = df_train.values\n",
    "y_test = df_test.pop('kwh')\n",
    "X_test = df_test.values\n",
    "\n",
    "# y_train = np.log(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mean_absolute_percentage_error(y_true, y_pred): \n",
    "    \"\"\"\n",
    "    Returns MAPE.\n",
    "    \"\"\"\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.795359876288632"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_pred) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x119eac950>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8KgBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1184224d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.plot(y_test.index, y_test, label = 'test')\n",
    "ax.plot(y_test.index, y_pred, label = 'pred')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
